EDBT 2026 Demo / reviewers in the wild / expert

Rongmei Li

dblp:79/5721 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › retrieval models › language model
parsimonious language model		 0.112008
Using parsimonious language models on web data · SIGIR 2008
Information retrieval
retrieval models		 0.112008
Using parsimonious language models on web data · SIGIR 2008
Information retrieval › web search
web information retrieval		 0.012008
Using parsimonious language models on web data · SIGIR 2008

Methods — techniques the papers use, named apart from their topics

language modeling · 0.1
YearPublicationVenuePosition
2008 Using parsimonious language models on web data
abstract
In this paper we explore the use of parsimonious language models for web retrieval. These models are smaller thus more efficient than the standard language models and are therefore well suited for large-scale web retrieval. We have conducted experiments on four TREC topic sets, and found that the parsimonious language model results in improvement of retrieval effectiveness over the standard language model for all data-sets and measures. In all cases the improvement is significant, and more substantial than in earlier experiments on newspaper/newswire data.
Rianne Kaptein, Rongmei Li, Djoerd Hiemstra, Jaap Kamps
 SIGIR2